Annotating Online Misogyny

Philine Zeinert, Nanna Inie, Leon Derczynski

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Abstrakt

Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse. This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
OriginalsprogEngelsk
TitelProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
ForlagAssociation for Computational Linguistics
Publikationsdato3 aug. 2021
Sider3181–3197
StatusUdgivet - 3 aug. 2021
Begivenhed59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing -
Varighed: 1 aug. 2021 → …

Konference

Konference59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
Periode01/08/2021 → …

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